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This chapter provides an overview of descriptive statistics, including graphical techniques and numerical measures, for summarizing data. It covers populations and samples, variable types, and the hierarchy of data. The chapter also explains the graphical and tabular techniques for nominal data.
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Chapter Two Graphical and Tabular Descriptive Techniques
Introduction & Re-cap… Descriptive statistics involves arranging, summarizing, and presenting a set of data in such a way that useful information is produced. Its methods make use of graphical techniques and numerical descriptive measures (such as averages) to summarize and present the data. Statistics Data Information
Populations & Samples The graphical & tabular methods presented here apply to both entire populations and samples drawn from populations. Population Sample Subset
Definitions… A variable is some characteristic of a population or sample. E.g. student grades. Typically denoted with a capital letter: X, Y, Z… The valuesof the variable are the range of possible values for a variable. E.g. student marks (0..100) Data are the observed values of a variable. E.g. student marks: {67, 74, 71, 83, 93, 55, 48}
Types of Data & Information Data (at least for purposes of Statistics) fall into three main groups: Interval Data Nominal Data Ordinal Data
Interval Data… Intervaldata • Real numbers, i.e. heights, weights, prices, etc. • Also referred to as quantitative or numerical. Arithmetic operations can be performed on Interval Data, thus its meaningful to talk about 2*Height, or Price + $1, and so on.
Nominal Data… Nominal Data • Thevalues of nominal data are categories. E.g. responses to questions about marital status, coded as: Single = 1, Married = 2, Divorced = 3, Widowed = 4 These data are categorical in nature; arithmetic operations don’t make any sense (e.g. does Widowed ÷ 2 = Married?!) Nominal data are also called qualitative or categorical.
Ordinal Data… OrdinalData appear to be categorical in nature, but their values have an order; a ranking to them: E.g. College course rating system: poor = 1, fair = 2, good = 3, very good = 4, excellent = 5 While it is still not meaningful to do arithmetic on this data (e.g. does 2*fair = very good?!), we can say things like: excellent > poor or fair < very good That is, order is maintained no matter what numeric values are assigned to each category.
Calculations for Types of Data As mentioned above, • All calculations are permitted on interval data. • Only calculations involving a ranking process are allowed for ordinal data. • No calculations are allowed for nominal data, save counting the number of observations in each category. This lends itself to the following “hierarchy of data”…
Hierarchy of Data… Interval Values are real numbers. All calculations are valid. Data may be treated as ordinal or nominal. Ordinal Values must represent the ranked order of the data. Calculations based on an ordering process are valid. Data may be treated as nominal but not as interval. Nominal Values are the arbitrary numbers that represent categories. Only calculations based on the frequencies of occurrence are valid. Data may not be treated as ordinal or interval.
Graphical & Tabular Techniques for Nominal Data… The only allowable calculation on nominal data is to count the frequency of each value of the variable. We can summarize the data in a table that presents the categories and their counts called a frequency distribution. A relative frequency distribution lists the categories and the proportion with which each occurs.
Example 2.1 Light Beer Preference Survey In 2006 total light beer sales in the United States was approximately 3 million gallons With this large a market breweries often need to know more about who is buying their product. The marketing manager of a major brewery wanted to analyze the light beer sales among college and university students who do drink light beer. A random sample of 285 graduating students was asked to report which of the following is their favorite light beer.
Example 2.1 1. Budweiser Light 2. Busch Light 3. Coors Light 4. Michelob Light 5. Miller Lite 6. Natural Light 7. Other brand The responses were recorded using the codes. Construct a frequency and relative frequency distribution for these data and graphically summarize the data by producing a bar chart and a pie chart.
Nominal Data (Frequency) Bar Charts are often used to display frequencies…
Nominal Data (Relative Frequency) Pie Charts show relative frequencies…
Nominal Data It all the same information, (based on the same data). Just different presentation.
Example 2.2 Table 2.3 lists the total energy consumption of the United States from all sources in 2005. To make it easier to see the details the table measures the heat content in metric tons (1,000 kilograms) of oil equivalent. For example, the United States burned an amount of coal and coal products equivalent to 545,259 metric tons of oil. Use an appropriate graphical technique to depict these figures.
Table 2.3 Xm02-02* Non-Renewable Energy Sources Heat Content Coal & coal products 545,258 Oil 903,440 Natural Gas 517,881 Nuclear 209,890 Renewable Energy Sources Hydroelectric 18,251 Solid Biomass 52,473 Other (Liquid biomass, geothermal, 20,533 solar, wind, and tide, wave, & Ocean) Total 2,267,726
Example 2.2 Energy Consumption
Graphical Techniques for Interval Data There are several graphical methods that are used when the data are interval (i.e. numeric, non-categorical). The most important of these graphical methods is the histogram. The histogram is not only a powerful graphical technique used to summarize interval data, but it is also used to help explain probabilities.
Example 2.4 Following deregulation of telephone service, several new companies were created to compete in the business of providing long-distance telephone service. In almost all cases these companies competed on price since the service each offered is similar. Pricing a service or product in the face of stiff competition is very difficult. Factors to be considered include supply, demand, price elasticity, and the actions of competitors. Long-distance packages may employ per-minute charges, a flat monthly rate, or some combination of the two. Determining the appropriate rate structure is facilitated by acquiring information about the behaviors of customers and in particular the size of monthly long-distance bills.
Example 2.4 As part of a larger study, a long-distance company wanted to acquire information about the monthly bills of new subscribers in the first month after signing with the company. The company’s marketing manager conducted a survey of 200 new residential subscribers wherein the first month’s bills were recorded. These data are stored in file Xm02-04. The general manager planned to present his findings to senior executives. What information can be extracted from these data?
Example 2.4 In Example 2.1 we created a frequency distribution of the 5 categories. In this example we also create a frequency distribution by counting the number of observations that fall into a series of intervals, called classes.
Example 2.4 We have chosen eight classes defined in such a way that each observation falls into one and only one class. These classes are defined as follows: Classes Amounts that are less than or equal to 15 Amounts that are more than 15 but less than or equal to 30 Amounts that are more than 30 but less than or equal to 45 Amounts that are more than 45 but less than or equal to 60 Amounts that are more than 60 but less than or equal to 75 Amounts that are more than 75 but less than or equal to 90 Amounts that are more than 90 but less than or equal to 105 Amounts that are more than 105 but less than or equal to 120
Interpret… (18+28+14=60)÷200 = 30% i.e. nearly a third of the phone bills are $90 or more. about half (71+37=108) of the bills are “small”, i.e. less than $30 There are only a few telephone bills in the middle range.
Building a Histogram… • Collect the Data • Create a frequency distribution for the data… How? a) Determine the number of classes to use… How? Refer to table 2.6: With 200 observations, we should have between 7 & 10 classes… Alternative, we could use Sturges’ formula: Number of class intervals = 1 + 3.3 log (n)
Building a Histogram… • Collect the Data • Create a frequency distribution for the data… How? a) Determine the number of classes to use. [8] b) Determine how large to make each class… How? Look at the range of the data, that is, Range = Largest Observation – Smallest Observation Range = $119.63 – $0 = $119.63 Then each class width becomes: Range ÷ (# classes) = 119.63 ÷ 8 ≈ 15
Shapes of Histograms… Symmetry A histogram is said to be symmetric if, when we draw a vertical line down the center of the histogram, the two sides are identical in shape and size: Frequency Frequency Frequency Variable Variable Variable
Shapes of Histograms… Skewness A skewed histogram is one with a long tail extending to either the right or the left: Frequency Frequency Variable Variable Positively Skewed Negatively Skewed
Shapes of Histograms… Modality A unimodal histogram is one with a single peak, while a bimodal histogram is one with two peaks: Bimodal Unimodal Frequency Frequency Variable Variable A modal class is the class with the largest number of observations
Shapes of Histograms… Bell Shape A special type of symmetricunimodal histogram is one that is bell shaped: Frequency Many statistical techniques require that the population be bell shaped. Drawing the histogram helps verify the shape of the population in question. Variable Bell Shaped
Histogram Comparison… Compare & contrast the following histograms based on data from Ex. 2.6 & Ex. 2.7: The two courses, Business Statistics and Mathematical Statistics have very different histograms… unimodal vs. bimodal spread of the marks (narrower | wider)
Stem & Leaf Display… • Retains information about individual observations that would normally be lost in the creation of a histogram. • Split each observation into two parts, a stem and a leaf: • e.g. Observation value: 42.19 • There are several ways to split it up… • We could split it at the decimal point: • Or split it at the “tens” position (while rounding to the nearest integer in the “ones” position)
Stem & Leaf Display… • Continue this process for all the observations. Then, use the “stems” for the classes and each leaf becomes part of the histogram (based on Example 2.4 data) as follows… Stem Leaf0 00000000001111122222233333455555566666667788889999991 0000011112333333344555556678899992 00001111123446667789993 0013355894 1244455895 335666 34587 0222245567898 3344578899999 0011222223334455599910 00134444669911 124557889 Thus, we still have access to our original data point’s value!
Histogram and Stem & Leaf… Compare the overall shapes of the figures…
Ogive… • (pronounced “Oh-jive”) is a graph of a cumulativefrequency distribution. • We create an ogive in three steps… • First, from the frequency distribution created earlier, calculate relative frequencies: • Relative Frequency = # of observations in a class Total # of observations
Relative Frequencies… • For example, we had 71 observations in our first class (telephone bills from $0.00 to $15.00). Thus, the relative frequency for this class is 71 ÷ 200 (the total # of phone bills) = 0.355 (or 35.5%)
Ogive… • Is a graph of a cumulativefrequency distribution. • We create an ogive in three steps… • 1) Calculate relative frequencies. • 2) Calculate cumulative relative frequencies by adding the current class’ relative frequency to the previous class’ cumulative relative frequency. • (For the first class, its cumulative relative frequency is just its relative frequency)
Cumulative Relative Frequencies… first class… next class: .355+.185=.540 : : last class: .930+.070=1.00
Ogive… • Is a graph of a cumulativefrequency distribution. • 1) Calculate relative frequencies. • 2) Calculate cumulative relative frequencies. • 3) Graph the cumulative relative frequencies…
Ogive… The ogive can be used to answer questions like: What telephone bill value is at the 50th percentile? “around $35” (Refer also to Fig. 2.13 in your textbook)
Describing Time Series Data Observations measured at the same point in time are called cross-sectional data. Observations measured at successive points in time are called time-series data. Time-series data graphed on a line chart, which plots the value of the variable on the vertical axis against the time periods on the horizontal axis.
Example 2.8 We recorded the monthly average retail price of gasoline since 1978. Xm02-08 Draw a line chart to describe these data and briefly describe the results.
Example 2.8 Monthly Average Retail Price of Gasoline Price in Dollars Months from January 1978 to June 2006
Example 2.9 Price of Gasoline in 1982-84 Constant Dollars Xm02-09 Remove the effect of inflation in Example 2.8 to determine whether gasoline prices are higher than they have been in the past after removing the effect of inflation.
